Published online Feb 28, 2021. doi: 10.35711/aimi.v2.i1.5
Peer-review started: November 28, 2020
First decision: December 18, 2020
Revised: December 31, 2020
Accepted: February 12, 2021
Article in press: February 12, 2021
Published online: February 28, 2021
The use of artificial intelligence (AI) in ophthalmology is not very new and its use is expanding into various subspecialties of the eye like retina and glaucoma, thereby helping ophthalmologists to diagnose and treat diseases better than before. Incorporating “deep learning” (a subfield of AI) into image-based systems such as optical coherence tomography has dramatically improved the machine's ability to screen and identify stages of diabetic retinopathy accurately. Similar applications have been tried in the field of retinopathy of prematurity and age-related macular degeneration, a silent retinal condition that needs to be diagnosed early to prevent progression. The advent of AI into glaucoma diagnostics in analyzing visual fields and assessing disease progression also holds a promising role. The ability of the software to detect even a subtle defect that the human eye can miss has led to a revolution in the management of certain ocular conditions. However, there are few significant challenges in the AI systems, such as the incorporation of quality images, training sets and the black box dilemma. Nevertheless, despite the existing differences, there is always a chance of improving the machines/software to potentiate their efficacy and standards. This review article shall discuss the current applications of AI in ophthalmology, significant challenges and the prospects as to how both science and medicine can work together.
Core Tip: Artificial intelligence has improved the diagnostic ability in the ophthalmology field, thereby improving patient care. The in-depth image recognition in diabetic retinopathy, retinopathy of prematurity and age-related macular degeneration has helped in early diagnosis and prevention. The detection of visual filed defect even at its minute stage in glaucoma and other ocular conditions has accurately staged the disease with the prediction of its severity. Still, many challenges need to be addressed, such as image incorporation, training sets and the black box dilemma. Nevertheless, despite the existing differences, there is always a chance of improving machines to potentiate their efficacy and standards.